filter_audioset_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 995,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import subprocess\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 996,
   "metadata": {},
   "outputs": [],
   "source": [
    "audioset_labels = pd.read_csv(\"data/class_labels_indices.csv\")\n",
    "qa_true_count = pd.read_csv(\"data/qa_true_counts.csv\")\n",
    "original_labels = audioset_labels.copy()\n",
    "# audioset_unbal_train_df = pd.read_csv(\"data/audioset/unbalanced_train_segments.csv\")\n",
    "audioset_bal_train = pd.read_csv(\"data/balanced_train_segments-edited.csv\")#, delimiter=', ', engine='python')\n",
    "audioset_eval = pd.read_csv(\"data/eval_segments-edited.csv\")#, delimiter=', ', engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22160, 15)"
      ]
     },
     "execution_count": 997,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audioset_bal_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['# YTID', ' start_seconds', ' end_seconds', ' positive_labels', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14']\n",
      "['# YTID', ' start_seconds', ' end_seconds', ' positive_labels', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13']\n"
     ]
    }
   ],
   "source": [
    "print(list(audioset_bal_train.columns.values))\n",
    "print(list(audioset_eval.columns.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 999,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[' positive_labels', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14']\n"
     ]
    }
   ],
   "source": [
    "train_cols = list(audioset_bal_train.columns.values)[3:]\n",
    "print(train_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[' positive_labels', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13']\n"
     ]
    }
   ],
   "source": [
    "eval_cols = list(audioset_eval.columns.values)[3:]\n",
    "print(eval_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1001,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create column with all labels\n",
    "audioset_bal_train.fillna('', inplace=True)\n",
    "audioset_eval.fillna('', inplace=True)\n",
    "audioset_bal_train['all_labels'] = audioset_bal_train[train_cols].apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n",
    "audioset_eval['all_labels'] = audioset_bal_train[eval_cols].apply(lambda row: ','.join(row.values.astype(str)), axis=1)\n",
    "audioset_bal_train['all_labels'] = audioset_bal_train['all_labels'].str.rstrip(',')\n",
    "audioset_eval['all_labels'] = audioset_eval['all_labels'].str.rstrip(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1055,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define some helper functions\n",
    "def contains_animal_label(positive_labels):\n",
    "    for label in label_list:\n",
    "        if label in positive_labels:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def contains_only_animal_label(positive_labels):\n",
    "    for label in label_list:\n",
    "        if label not in positive_labels:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "def get_display_name_from_mid(mid):\n",
    "    return original_labels.loc[original_labels['mid'] == mid].iloc[0]['display_name']\n",
    "\n",
    "def get_true_count_from_mid(mid):\n",
    "    return qa_true_count.loc[qa_true_count['label_id'] == mid].iloc[0]['num_true']\n",
    "\n",
    "\n",
    "def strip_quotes(in_string):\n",
    "    in_string = in_string.strip()\n",
    "    if in_string.startswith('\"'):\n",
    "        in_string = in_string[1:]\n",
    "    if in_string.endswith('\"'):\n",
    "        in_string = in_string[:-1]\n",
    "    return in_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1003,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Strip quotes from every column. They are no longer needed\n",
    "for col in train_cols:\n",
    "    audioset_bal_train[col] = audioset_bal_train[col].apply(strip_quotes)\n",
    "for col in eval_cols:\n",
    "    audioset_eval[col] = audioset_eval[col].apply(strip_quotes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1004,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['# YTID', ' start_seconds', ' end_seconds', ' positive_labels', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14', 'all_labels']\n",
      "['# YTID', ' start_seconds', ' end_seconds', ' positive_labels', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14', 'all_labels']\n"
     ]
    }
   ],
   "source": [
    "train_cols = list(audioset_bal_train.columns.values)\n",
    "print(train_cols)\n",
    "\n",
    "eval_cols = list(audioset_eval.columns.values)\n",
    "print(train_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1005,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rearragne columns\n",
    "audioset_bal_train = audioset_bal_train[['# YTID', ' start_seconds', ' end_seconds', 'all_labels', ' positive_labels', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14']]\n",
    "audioset_eval = audioset_eval[['# YTID', ' start_seconds', ' end_seconds', 'all_labels', ' positive_labels', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1006,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rearragne columns\n",
    "audioset_bal_train.rename(columns={\" positive_labels\": 'Unnamed: 3'}, inplace=True)\n",
    "audioset_eval.rename(columns={\" positive_labels\": 'Unnamed: 3'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1007,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># YTID</th>\n",
       "      <th>start_seconds</th>\n",
       "      <th>end_seconds</th>\n",
       "      <th>all_labels</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>\"/m/09x0r,/t/dd00088\"</td>\n",
       "      <td>/m/09x0r</td>\n",
       "      <td>/t/dd00088</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>\"/m/012xff\"</td>\n",
       "      <td>/m/012xff</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>\"/m/03fwl,/m/04rlf,/m/09x0r\"</td>\n",
       "      <td>/m/03fwl</td>\n",
       "      <td>/m/04rlf</td>\n",
       "      <td>/m/09x0r</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>\"/t/dd00003,/t/dd00005\"</td>\n",
       "      <td>/t/dd00003</td>\n",
       "      <td>/t/dd00005</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>200.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>\"/m/032s66,/m/073cg4\"</td>\n",
       "      <td>/m/032s66</td>\n",
       "      <td>/m/073cg4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   # YTID   start_seconds   end_seconds                     all_labels  \\\n",
       "0  #NAME?            30.0          40.0          \"/m/09x0r,/t/dd00088\"   \n",
       "1  #NAME?            50.0          60.0                    \"/m/012xff\"   \n",
       "2  #NAME?             0.0          10.0   \"/m/03fwl,/m/04rlf,/m/09x0r\"   \n",
       "3  #NAME?            30.0          40.0        \"/t/dd00003,/t/dd00005\"   \n",
       "4  #NAME?           200.0         210.0          \"/m/032s66,/m/073cg4\"   \n",
       "\n",
       "   Unnamed: 3  Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8  \\\n",
       "0    /m/09x0r  /t/dd00088                                               \n",
       "1   /m/012xff                                                           \n",
       "2    /m/03fwl    /m/04rlf   /m/09x0r                                    \n",
       "3  /t/dd00003  /t/dd00005                                               \n",
       "4   /m/032s66   /m/073cg4                                               \n",
       "\n",
       "  Unnamed: 9 Unnamed: 10 Unnamed: 11 Unnamed: 12 Unnamed: 13 Unnamed: 14  \n",
       "0                                                                         \n",
       "1                                                                         \n",
       "2                                                                         \n",
       "3                                                                         \n",
       "4                                                                         "
      ]
     },
     "execution_count": 1007,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audioset_bal_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1008,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>mid</th>\n",
       "      <th>display_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>/m/09x0r</td>\n",
       "      <td>Speech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>/m/05zppz</td>\n",
       "      <td>Male speech, man speaking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>/m/02zsn</td>\n",
       "      <td>Female speech, woman speaking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>/m/0ytgt</td>\n",
       "      <td>Child speech, kid speaking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>/m/01h8n0</td>\n",
       "      <td>Conversation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        mid                   display_name\n",
       "0      0   /m/09x0r                         Speech\n",
       "1      1  /m/05zppz      Male speech, man speaking\n",
       "2      2   /m/02zsn  Female speech, woman speaking\n",
       "3      3   /m/0ytgt     Child speech, kid speaking\n",
       "4      4  /m/01h8n0                   Conversation"
      ]
     },
     "execution_count": 1008,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audioset_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1009,
   "metadata": {},
   "outputs": [],
   "source": [
    " index_list = [\n",
    " 72,\n",
    " 73,\n",
    " 74,\n",
    " 75,\n",
    " 77,\n",
    " 79,\n",
    " 80,\n",
    " 81,\n",
    " 82,\n",
    " 83,\n",
    " 84,\n",
    " 85,\n",
    " 86,\n",
    " 87,\n",
    " 88,\n",
    " 89,\n",
    " 90,\n",
    " 91,\n",
    " 93,\n",
    " 94,\n",
    " 95,\n",
    " 96,\n",
    " 97,\n",
    " 98,\n",
    " 99,\n",
    "100,\n",
    "101,\n",
    "102,\n",
    "103,\n",
    "104,\n",
    "105,\n",
    "106,\n",
    "108,\n",
    "109,\n",
    "110,\n",
    "111,\n",
    "112,\n",
    "113,\n",
    "114,\n",
    "115,\n",
    "116,\n",
    "117,\n",
    "118,\n",
    "119,\n",
    "120,\n",
    "121,\n",
    "122,\n",
    "123,\n",
    "124,\n",
    "125,\n",
    "126,\n",
    "127,\n",
    "128,\n",
    "129,\n",
    "130,\n",
    "131,\n",
    "132,\n",
    "133,\n",
    "134,\n",
    "135,\n",
    "136 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1010,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 1010,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(index_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1011,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61, 3)\n"
     ]
    }
   ],
   "source": [
    "# Only use animal labels\n",
    "audioset_labels = audioset_labels.iloc[index_list]\n",
    "print(audioset_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1012,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also\n",
    "#    print(audioset_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1013,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add column indicating if data has a single label or multiple labels\n",
    "#  audioset_bal_train['singlelabel'] = audioset_bal_train[' positive_labels'].str.endswith('\"')\n",
    "#  audioset_eval['singlelabel'] = audioset_bal_train[' positive_labels'].str.endswith('\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1014,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (22160, 16)\n",
      "Eval shape: (20373, 15)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train shape:\", audioset_bal_train.shape)\n",
    "print(\"Eval shape:\", audioset_eval.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1015,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/m/0jbk', '/m/068hy', '/m/0bt9lr', '/m/05tny_', '/m/07qf0zm', '/m/0ghcn6', '/t/dd00136', '/m/01yrx', '/m/02yds9', '/m/07qrkrw', '/m/07rjwbb', '/m/07r81j2', '/m/0ch8v', '/m/03k3r', '/m/07rv9rh', '/m/07q5rw0', '/m/01xq0k1', '/m/07rpkh9', '/m/068zj', '/t/dd00018', '/m/03fwl', '/m/07q0h5t', '/m/07bgp', '/m/025rv6n', '/m/09b5t', '/m/07st89h', '/m/07qn5dc', '/m/01rd7k', '/m/07svc2k', '/m/09ddx', '/m/07qdb04', '/m/0dbvp', '/m/01280g', '/m/0cdnk', '/m/04cvmfc', '/m/015p6', '/m/020bb7', '/m/07pggtn', '/m/07sx8x_', '/m/0h0rv', '/m/07r_25d', '/m/04s8yn', '/m/07r5c2p', '/m/09d5_', '/m/07r_80w', '/m/05_wcq', '/m/01z5f', '/m/06hps', '/m/04rmv', '/m/07r4gkf', '/m/03vt0', '/m/09xqv', '/m/09f96', '/m/0h2mp', '/m/07pjwq1', '/m/01h3n', '/m/09ld4', '/m/07st88b', '/m/078jl', '/m/07qn4z3', '/m/032n05']\n"
     ]
    }
   ],
   "source": [
    "label_list = audioset_labels['mid'].tolist()\n",
    "print(label_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># YTID</th>\n",
       "      <th>start_seconds</th>\n",
       "      <th>end_seconds</th>\n",
       "      <th>all_labels</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>\"/m/09x0r,/t/dd00088\"</td>\n",
       "      <td>/m/09x0r</td>\n",
       "      <td>/t/dd00088</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>\"/m/012xff\"</td>\n",
       "      <td>/m/012xff</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>\"/m/03fwl,/m/04rlf,/m/09x0r\"</td>\n",
       "      <td>/m/03fwl</td>\n",
       "      <td>/m/04rlf</td>\n",
       "      <td>/m/09x0r</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>\"/t/dd00003,/t/dd00005\"</td>\n",
       "      <td>/t/dd00003</td>\n",
       "      <td>/t/dd00005</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>200.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>\"/m/032s66,/m/073cg4\"</td>\n",
       "      <td>/m/032s66</td>\n",
       "      <td>/m/073cg4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   # YTID   start_seconds   end_seconds                     all_labels  \\\n",
       "0  #NAME?            30.0          40.0          \"/m/09x0r,/t/dd00088\"   \n",
       "1  #NAME?            50.0          60.0                    \"/m/012xff\"   \n",
       "2  #NAME?             0.0          10.0   \"/m/03fwl,/m/04rlf,/m/09x0r\"   \n",
       "3  #NAME?            30.0          40.0        \"/t/dd00003,/t/dd00005\"   \n",
       "4  #NAME?           200.0         210.0          \"/m/032s66,/m/073cg4\"   \n",
       "\n",
       "   Unnamed: 3  Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8  \\\n",
       "0    /m/09x0r  /t/dd00088                                               \n",
       "1   /m/012xff                                                           \n",
       "2    /m/03fwl    /m/04rlf   /m/09x0r                                    \n",
       "3  /t/dd00003  /t/dd00005                                               \n",
       "4   /m/032s66   /m/073cg4                                               \n",
       "\n",
       "  Unnamed: 9 Unnamed: 10 Unnamed: 11 Unnamed: 12 Unnamed: 13 Unnamed: 14  \n",
       "0                                                                         \n",
       "1                                                                         \n",
       "2                                                                         \n",
       "3                                                                         \n",
       "4                                                                         "
      ]
     },
     "execution_count": 1016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audioset_bal_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "metadata": {},
   "outputs": [],
   "source": [
    "audioset_bal_train['animal_sound'] = audioset_bal_train['all_labels'].apply(contains_animal_label)\n",
    "audioset_eval['animal_sound'] = audioset_eval['all_labels'].apply(contains_animal_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1018,
   "metadata": {},
   "outputs": [],
   "source": [
    "audioset_bal_train['only_animal'] = audioset_bal_train['all_labels'].apply(contains_only_animal_label)\n",
    "audioset_eval['only_animal'] = audioset_eval['all_labels'].apply(contains_only_animal_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced train data:\n",
      "(2391, 18)\n",
      "Eval data\n",
      "(2206, 17)\n"
     ]
    }
   ],
   "source": [
    "# Fitler all non-animal data\n",
    "#audioset_bal_train = audioset_bal_train[audioset_bal_train[' positive_labels'].isin(label_list)]\n",
    "audioset_bal_train = audioset_bal_train[audioset_bal_train['animal_sound'] == True]\n",
    "print(\"Balanced train data:\")\n",
    "print(audioset_bal_train.shape)\n",
    "#print(audioset_bal_train.head())\n",
    "\n",
    "#  print(\"Unbalanced train data:\")\n",
    "#  audioset_unbal_train[audioset_unbal_train[' positive_labels'].isin(label_list)]\n",
    "#  audioset_unbal_train.shape\n",
    "\n",
    "audioset_eval = audioset_eval[audioset_eval['animal_sound'] == True]\n",
    "print(\"Eval data\")\n",
    "print(audioset_eval.shape)\n",
    "#print(audioset_eval.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced train data:\n",
      "(0, 18)\n"
     ]
    }
   ],
   "source": [
    "# Is there any data will only animal sounds?....No? Maybe I scrubbed off too many labels when editing\n",
    "only_animal = audioset_bal_train[audioset_bal_train['only_animal'] == True]\n",
    "print(\"Balanced train data:\")\n",
    "print(only_animal.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1021,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter all wavs with multiple labels\n",
    "#audioset_bal_train = audioset_bal_train.loc[temp_df['singlelabel'] == True]\n",
    "#print(\"Balanced train data:\")\n",
    "#print(audioset_bal_train.shape)\n",
    "\n",
    "#  print(\"Unbalanced train data:\")\n",
    "#  audioset_unbal_train[audioset_unbal_train[' positive_labels'].isin(label_list)]\n",
    "#  audioset_unbal_train.shape\n",
    "\n",
    "#audioset_eval = audioset_eval.loc[audioset_eval['singlelabel'] == False]\n",
    "#print(\"Eval data\")\n",
    "#print(audioset_eval.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save filtered data frames\n",
    "audioset_bal_train.to_csv('data/balanced_train_segments-animals.csv', header=True, index=False)\n",
    "audioset_eval.to_csv('data/eval_segments-animals.csv', header=True, index=False)\n",
    "audioset_labels.to_csv('data/class_labels_indices-animals.csv', header=True, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1023,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina' #set 'png' here when working on notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Everything after this needs to be updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1024,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># YTID</th>\n",
       "      <th>start_seconds</th>\n",
       "      <th>end_seconds</th>\n",
       "      <th>all_labels</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>animal_sound</th>\n",
       "      <th>only_animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#NAME?</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>\"/m/03fwl,/m/04rlf,/m/09x0r\"</td>\n",
       "      <td>/m/03fwl</td>\n",
       "      <td>/m/04rlf</td>\n",
       "      <td>/m/09x0r</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-11LhdJgBb8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>\"/m/04rlf,/m/07qn4z3\"</td>\n",
       "      <td>/m/04rlf</td>\n",
       "      <td>/m/07qn4z3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-3IYpJfLVJk</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>\"/m/03k3r,/m/07q5rw0,/m/0jbk\"</td>\n",
       "      <td>/m/03k3r</td>\n",
       "      <td>/m/07q5rw0</td>\n",
       "      <td>/m/0jbk</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>-64xnyOswXA</td>\n",
       "      <td>120.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>\"/m/0cdnk\"</td>\n",
       "      <td>/m/0cdnk</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>-6x2PtSRfJU</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>\"/m/01yrx,/m/068hy,/m/07qrkrw,/m/09x0r\"</td>\n",
       "      <td>/m/01yrx</td>\n",
       "      <td>/m/068hy</td>\n",
       "      <td>/m/07qrkrw</td>\n",
       "      <td>/m/09x0r</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         # YTID   start_seconds   end_seconds  \\\n",
       "2        #NAME?             0.0          10.0   \n",
       "15  -11LhdJgBb8            30.0          40.0   \n",
       "29  -3IYpJfLVJk            30.0          40.0   \n",
       "45  -64xnyOswXA           120.0         130.0   \n",
       "54  -6x2PtSRfJU            30.0          40.0   \n",
       "\n",
       "                                  all_labels Unnamed: 3  Unnamed: 4  \\\n",
       "2               \"/m/03fwl,/m/04rlf,/m/09x0r\"   /m/03fwl    /m/04rlf   \n",
       "15                     \"/m/04rlf,/m/07qn4z3\"   /m/04rlf  /m/07qn4z3   \n",
       "29             \"/m/03k3r,/m/07q5rw0,/m/0jbk\"   /m/03k3r  /m/07q5rw0   \n",
       "45                                \"/m/0cdnk\"   /m/0cdnk               \n",
       "54   \"/m/01yrx,/m/068hy,/m/07qrkrw,/m/09x0r\"   /m/01yrx    /m/068hy   \n",
       "\n",
       "    Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9 Unnamed: 10  \\\n",
       "2     /m/09x0r                                                           \n",
       "15                                                                       \n",
       "29     /m/0jbk                                                           \n",
       "45                                                                       \n",
       "54  /m/07qrkrw   /m/09x0r                                                \n",
       "\n",
       "   Unnamed: 11 Unnamed: 12 Unnamed: 13 Unnamed: 14  animal_sound  only_animal  \n",
       "2                                                           True        False  \n",
       "15                                                          True        False  \n",
       "29                                                          True        False  \n",
       "45                                                          True        False  \n",
       "54                                                          True        False  "
      ]
     },
     "execution_count": 1024,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create temp_df so that we can scrub the positive_label column and perform some operations to view the data\n",
    "temp_df = audioset_bal_train.copy()\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1025,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>/m/04rlf</th>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/015p6</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/068hy</th>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/025rv6n</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/01yrx</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 3\n",
       "/m/04rlf           193\n",
       "/m/015p6           171\n",
       "/m/068hy           157\n",
       "/m/025rv6n         133\n",
       "/m/01yrx           120"
      ]
     },
     "execution_count": 1025,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = temp_df['Unnamed: 3'].value_counts().to_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1026,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 3'], dtype='object')"
      ]
     },
     "execution_count": 1026,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1027,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>mid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>/m/04rlf</th>\n",
       "      <td>193</td>\n",
       "      <td>/m/04rlf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/015p6</th>\n",
       "      <td>171</td>\n",
       "      <td>/m/015p6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/068hy</th>\n",
       "      <td>157</td>\n",
       "      <td>/m/068hy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/025rv6n</th>\n",
       "      <td>133</td>\n",
       "      <td>/m/025rv6n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/01yrx</th>\n",
       "      <td>120</td>\n",
       "      <td>/m/01yrx</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 3         mid\n",
       "/m/04rlf           193    /m/04rlf\n",
       "/m/015p6           171    /m/015p6\n",
       "/m/068hy           157    /m/068hy\n",
       "/m/025rv6n         133  /m/025rv6n\n",
       "/m/01yrx           120    /m/01yrx"
      ]
     },
     "execution_count": 1027,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.reset_index(inplace=True)\n",
    "df['mid'] = df.index\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.columns = ['mid', 'count']\n",
    "df.rename(columns={'Unnamed: 3': 'count'}, inplace=True)\n",
    "df['display_name'] = df['mid'].apply(get_display_name_from_mid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mid</th>\n",
       "      <th>display_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>/m/04rlf</th>\n",
       "      <td>193</td>\n",
       "      <td>/m/04rlf</td>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/015p6</th>\n",
       "      <td>171</td>\n",
       "      <td>/m/015p6</td>\n",
       "      <td>Bird</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/068hy</th>\n",
       "      <td>157</td>\n",
       "      <td>/m/068hy</td>\n",
       "      <td>Domestic animals, pets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/025rv6n</th>\n",
       "      <td>133</td>\n",
       "      <td>/m/025rv6n</td>\n",
       "      <td>Fowl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/m/01yrx</th>\n",
       "      <td>120</td>\n",
       "      <td>/m/01yrx</td>\n",
       "      <td>Cat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mid            display_name\n",
       "/m/04rlf      193    /m/04rlf                   Music\n",
       "/m/015p6      171    /m/015p6                    Bird\n",
       "/m/068hy      157    /m/068hy  Domestic animals, pets\n",
       "/m/025rv6n    133  /m/025rv6n                    Fowl\n",
       "/m/01yrx      120    /m/01yrx                     Cat"
      ]
     },
     "execution_count": 1029,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['count', 'mid', 'display_name'], dtype='object')"
      ]
     },
     "execution_count": 1030,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            count         mid                             display_name\n",
      "/m/04rlf      193    /m/04rlf                                    Music\n",
      "/m/015p6      171    /m/015p6                                     Bird\n",
      "/m/068hy      157    /m/068hy                   Domestic animals, pets\n",
      "/m/025rv6n    133  /m/025rv6n                                     Fowl\n",
      "/m/01yrx      120    /m/01yrx                                      Cat\n",
      "/m/03vt0      104    /m/03vt0                                   Insect\n",
      "/m/03k3r       77    /m/03k3r                                    Horse\n",
      "/m/01xq0k1     62  /m/01xq0k1                          Cattle, bovinae\n",
      "/m/01280g      61   /m/01280g                             Wild animals\n",
      "/m/07r_80w     60  /m/07r_80w                                     Hoot\n",
      "/m/01h3n       60    /m/01h3n                          Bee, wasp, etc.\n",
      "/m/032n05      59   /m/032n05                       Whale vocalization\n",
      "/m/07st88b     59  /m/07st88b                                    Croak\n",
      "/m/078jl       59    /m/078jl                                    Snake\n",
      "/m/01z5f       59    /m/01z5f                    Canidae, dogs, wolves\n",
      "/m/05_wcq      56   /m/05_wcq              Bird flight, flapping wings\n",
      "/m/03fwl       56    /m/03fwl                                     Goat\n",
      "/m/06hps       55    /m/06hps                      Rodents, rats, mice\n",
      "/m/04rmv       48    /m/04rmv                                    Mouse\n",
      "/m/01rd7k      47   /m/01rd7k                                   Turkey\n",
      "/m/068zj       43    /m/068zj                                      Pig\n",
      "/m/04s8yn      43   /m/04s8yn                                     Crow\n",
      "/m/07r4gkf     39  /m/07r4gkf                                   Patter\n",
      "/m/07sx8x_     39  /m/07sx8x_                                   Squawk\n",
      "/m/05tny_      39   /m/05tny_                                     Bark\n",
      "/m/07bgp       37    /m/07bgp                                    Sheep\n",
      "/m/04cvmfc     35  /m/04cvmfc                                     Roar\n",
      "/m/07qn4z3     35  /m/07qn4z3                                   Rattle\n",
      "/m/09x0r       34    /m/09x0r                                   Speech\n",
      "/m/07qdb04     32  /m/07qdb04                                    Quack\n",
      "/m/07pjwq1     32  /m/07pjwq1                                     Buzz\n",
      "/m/06hck5      31   /m/06hck5                            Steam whistle\n",
      "/m/02yds9      28   /m/02yds9                                     Purr\n",
      "/m/07rv9rh     16  /m/07rv9rh                                Clip-clop\n",
      "/m/01g50p      13   /m/01g50p                Railroad car, train wagon\n",
      "/t/dd00018     13  /t/dd00018                                     Oink\n",
      "/m/07qf0zm      9  /m/07qf0zm                                     Howl\n",
      "/m/07q5rw0      9  /m/07q5rw0                            Neigh, whinny\n",
      "/m/06wzb        9    /m/06wzb                                    Steam\n",
      "/m/0ghcn6       7   /m/0ghcn6                                 Growling\n",
      "/m/01d3sd       7   /m/01d3sd                                  Snoring\n",
      "/m/0239kh       6   /m/0239kh                                  Cowbell\n",
      "/m/020bb7       6   /m/020bb7  Bird vocalization, bird call, bird song\n",
      "/m/01b_21       6   /m/01b_21                                    Cough\n",
      "/m/0cdnk        6    /m/0cdnk             Roaring cats (lions, tigers)\n",
      "/m/07qrkrw      6  /m/07qrkrw                                     Meow\n",
      "/m/07r5c2p      6  /m/07r5c2p                                      Caw\n",
      "/m/09ddx        5    /m/09ddx                                     Duck\n",
      "/m/02mk9        5    /m/02mk9                                   Engine\n",
      "/m/05r5wn       5   /m/05r5wn                      Rattle (instrument)\n",
      "/m/01j3sz       5   /m/01j3sz                                 Laughter\n",
      "/m/07q0h5t      5  /m/07q0h5t                                    Bleat\n",
      "/m/04zmvq       4   /m/04zmvq                            Train whistle\n",
      "/m/07r4k75      4  /m/07r4k75                                    Grunt\n",
      "/m/07r81j2      4  /m/07r81j2                                Caterwaul\n",
      "/m/04k94        3    /m/04k94                                   Liquid\n",
      "/m/03wwcy       3   /m/03wwcy                                 Doorbell\n",
      "/m/07pbtc8      3  /m/07pbtc8                          Walk, footsteps\n",
      "/m/07qmpdm      3  /m/07qmpdm                                  Clatter\n",
      "/m/02_nn        3    /m/02_nn                                     Fart\n",
      "/m/07q0yl5      2  /m/07q0yl5                                    Snort\n",
      "/m/07rc7d9      2  /m/07rc7d9                                  Bow-wow\n",
      "/m/07pyy8b      2  /m/07pyy8b                                     Pant\n",
      "/m/03qc9zr      2  /m/03qc9zr                                Screaming\n",
      "/m/03cczk       2   /m/03cczk                     Chewing, mastication\n",
      "/m/07r5v4s      2  /m/07r5v4s                                     Drip\n",
      "/m/0bt9lr       2   /m/0bt9lr                                      Dog\n",
      "/m/07r_k2n      2  /m/07r_k2n                                      Yip\n",
      "/m/07p6mqd      2  /m/07p6mqd                                    Slosh\n",
      "/m/04_sv        2    /m/04_sv                               Motorcycle\n",
      "/m/02x984l      2  /m/02x984l                           Mechanical fan\n",
      "/m/01z47d       1   /m/01z47d                              Busy signal\n",
      "/m/028v0c       1   /m/028v0c                                  Silence\n",
      "/m/07p7b8y      1  /m/07p7b8y                       Fill (with liquid)\n",
      "/m/0jbk         1     /m/0jbk                                   Animal\n",
      "/m/01hnzm       1   /m/01hnzm                                 Ringtone\n",
      "/m/012xff       1   /m/012xff                               Toothbrush\n",
      "/m/0130jx       1   /m/0130jx                Sink (filling or washing)\n",
      "/m/06_y0by      1  /m/06_y0by                      Environmental noise\n",
      "/m/012n7d       1   /m/012n7d                        Ambulance (siren)\n",
      "/m/07pqc89      1  /m/07pqc89                         Trickle, dribble\n",
      "/m/02p3nc       1   /m/02p3nc                                   Hiccup\n",
      "/m/07pggtn      1  /m/07pggtn                             Chirp, tweet\n",
      "/m/03cl9h       1   /m/03cl9h           Ice cream truck, ice cream van\n",
      "/m/02rhddq      1  /m/02rhddq                          Reversing beeps\n",
      "/m/0642b4       1   /m/0642b4                   Cupboard open or close\n",
      "/m/0199g        1    /m/0199g                                  Bicycle\n",
      "/m/02dgv        1    /m/02dgv                                     Door\n",
      "/m/015y_n       1   /m/015y_n                              Swing music\n",
      "/m/03p19w       1   /m/03p19w                               Jackhammer\n",
      "/m/07qfr4h      1  /m/07qfr4h      Hubbub, speech noise, speech babble\n",
      "/m/026z9        1    /m/026z9                                    Disco\n",
      "/m/02_41        1    /m/02_41                                     Fire\n",
      "/m/046dlr       1   /m/046dlr                              Alarm clock\n",
      "/m/0284vy3      1  /m/0284vy3                               Train horn\n",
      "/m/01h8n0       1   /m/01h8n0                             Conversation\n",
      "/m/05zppz       1   /m/05zppz                Male speech, man speaking\n",
      "/m/01jt3m       1   /m/01jt3m                             Toilet flush\n",
      "/m/09t49        1    /m/09t49                          Rustling leaves\n",
      "/m/02v2lh       1   /m/02v2lh                            New-age music\n",
      "/m/06h7j        1    /m/06h7j                                      Run\n",
      "/m/0342h        1    /m/0342h                                   Guitar\n",
      "/m/07ppn3j      1  /m/07ppn3j                                    Sniff\n",
      "/m/07svc2k      1  /m/07svc2k                                   Gobble\n",
      "/m/07mzm6       1   /m/07mzm6                                   Wheeze\n",
      "/m/07q4ntr      1  /m/07q4ntr                                   Bellow\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also\n",
    "    print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1032,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1355be4a8>"
      ]
     },
     "execution_count": 1032,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 444,
       "width": 378
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_df = df.loc[:, ['count', 'display_name','mid']]\n",
    "hist_df.set_index('display_name', inplace=True)\n",
    "hist_df['count'].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1033,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x133d9da58>"
      ]
     },
     "execution_count": 1033,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 382,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_df = hist_df[hist_df['count'] > 49 ]\n",
    "hist_df['count'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>display_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Music</th>\n",
       "      <td>193</td>\n",
       "      <td>/m/04rlf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bird</th>\n",
       "      <td>171</td>\n",
       "      <td>/m/015p6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domestic animals, pets</th>\n",
       "      <td>157</td>\n",
       "      <td>/m/068hy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fowl</th>\n",
       "      <td>133</td>\n",
       "      <td>/m/025rv6n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cat</th>\n",
       "      <td>120</td>\n",
       "      <td>/m/01yrx</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count         mid\n",
       "display_name                             \n",
       "Music                     193    /m/04rlf\n",
       "Bird                      171    /m/015p6\n",
       "Domestic animals, pets    157    /m/068hy\n",
       "Fowl                      133  /m/025rv6n\n",
       "Cat                       120    /m/01yrx"
      ]
     },
     "execution_count": 1034,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1035,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/m/04rlf', '/m/015p6', '/m/068hy', '/m/025rv6n', '/m/01yrx', '/m/03vt0', '/m/03k3r', '/m/01xq0k1', '/m/01280g', '/m/07r_80w', '/m/01h3n', '/m/032n05', '/m/07st88b', '/m/078jl', '/m/01z5f', '/m/05_wcq', '/m/03fwl', '/m/06hps']\n"
     ]
    }
   ],
   "source": [
    "label_list = [ label for label in hist_df['mid'].tolist() ]\n",
    "label_list \n",
    "print(label_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1036,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>mid</th>\n",
       "      <th>display_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>/m/0jbk</td>\n",
       "      <td>Animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>73</td>\n",
       "      <td>/m/068hy</td>\n",
       "      <td>Domestic animals, pets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>/m/0bt9lr</td>\n",
       "      <td>Dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>/m/05tny_</td>\n",
       "      <td>Bark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>/m/07qf0zm</td>\n",
       "      <td>Howl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index         mid            display_name\n",
       "72     72     /m/0jbk                  Animal\n",
       "73     73    /m/068hy  Domestic animals, pets\n",
       "74     74   /m/0bt9lr                     Dog\n",
       "75     75   /m/05tny_                    Bark\n",
       "77     77  /m/07qf0zm                    Howl"
      ]
     },
     "execution_count": 1036,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audioset_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1037,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Domestic animals, pets and wild animals. Categories are too broad\n",
    "#audioset_bal_train = audioset_bal_train[audioset_bal_train['Unnamed: 3'].isin(filter_list)]\n",
    "#audioset_eval = audioset_eval[audioset_eval['Unnamed: 3'].isin(filter_list)]\n",
    "#audioset_labels = audioset_labels[audioset_labels['mid'].isin(filter_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save filtered data frames\n",
    "#audioset_bal_train.to_csv('data/balanced_train_segments-animals.csv', header=True, index=False)\n",
    "#audioset_eval.to_csv('data/eval_segments-animals.csv', header=True, index=False)\n",
    "#audioset_labels.to_csv('data/class_labels_indices-animals.csv', header=True, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1039,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (2391, 18)\n",
      "Eval shape: (2206, 17)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train shape:\", audioset_bal_train.shape)\n",
    "print(\"Eval shape:\", audioset_eval.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1040,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Purr'"
      ]
     },
     "execution_count": 1040,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_display_name_from_mid('/m/02yds9')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1052,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label_id</th>\n",
       "      <th>num_rated</th>\n",
       "      <th>num_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/m/09x0r</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/m/05zppz</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/m/02zsn</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/m/0ytgt</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/m/01h8n0</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label_id  num_rated  num_true\n",
       "0   /m/09x0r         10        10\n",
       "1  /m/05zppz         10         9\n",
       "2   /m/02zsn         10        10\n",
       "3   /m/0ytgt          9         9\n",
       "4  /m/01h8n0         10         8"
      ]
     },
     "execution_count": 1052,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_true_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1054,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['label_id', 'num_rated', 'num_true'], dtype=object)"
      ]
     },
     "execution_count": 1054,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_true_count.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1049,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['count', 'mid'], dtype=object)"
      ]
     },
     "execution_count": 1049,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_df['true_count'] = hist_df['mid'].apply(get_true_count_from_mid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1061,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mid</th>\n",
       "      <th>true_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>display_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Music</th>\n",
       "      <td>193</td>\n",
       "      <td>/m/04rlf</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bird</th>\n",
       "      <td>171</td>\n",
       "      <td>/m/015p6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domestic animals, pets</th>\n",
       "      <td>157</td>\n",
       "      <td>/m/068hy</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fowl</th>\n",
       "      <td>133</td>\n",
       "      <td>/m/025rv6n</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cat</th>\n",
       "      <td>120</td>\n",
       "      <td>/m/01yrx</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insect</th>\n",
       "      <td>104</td>\n",
       "      <td>/m/03vt0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horse</th>\n",
       "      <td>77</td>\n",
       "      <td>/m/03k3r</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cattle, bovinae</th>\n",
       "      <td>62</td>\n",
       "      <td>/m/01xq0k1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wild animals</th>\n",
       "      <td>61</td>\n",
       "      <td>/m/01280g</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bee, wasp, etc.</th>\n",
       "      <td>60</td>\n",
       "      <td>/m/01h3n</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hoot</th>\n",
       "      <td>60</td>\n",
       "      <td>/m/07r_80w</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croak</th>\n",
       "      <td>59</td>\n",
       "      <td>/m/07st88b</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canidae, dogs, wolves</th>\n",
       "      <td>59</td>\n",
       "      <td>/m/01z5f</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whale vocalization</th>\n",
       "      <td>59</td>\n",
       "      <td>/m/032n05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snake</th>\n",
       "      <td>59</td>\n",
       "      <td>/m/078jl</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goat</th>\n",
       "      <td>56</td>\n",
       "      <td>/m/03fwl</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bird flight, flapping wings</th>\n",
       "      <td>56</td>\n",
       "      <td>/m/05_wcq</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rodents, rats, mice</th>\n",
       "      <td>55</td>\n",
       "      <td>/m/06hps</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count         mid  true_count\n",
       "display_name                                              \n",
       "Music                          193    /m/04rlf          10\n",
       "Bird                           171    /m/015p6          10\n",
       "Domestic animals, pets         157    /m/068hy           7\n",
       "Fowl                           133  /m/025rv6n           9\n",
       "Cat                            120    /m/01yrx          10\n",
       "Insect                         104    /m/03vt0           8\n",
       "Horse                           77    /m/03k3r           7\n",
       "Cattle, bovinae                 62  /m/01xq0k1           8\n",
       "Wild animals                    61   /m/01280g           8\n",
       "Bee, wasp, etc.                 60    /m/01h3n          10\n",
       "Hoot                            60  /m/07r_80w           5\n",
       "Croak                           59  /m/07st88b           9\n",
       "Canidae, dogs, wolves           59    /m/01z5f           8\n",
       "Whale vocalization              59   /m/032n05           4\n",
       "Snake                           59    /m/078jl           4\n",
       "Goat                            56    /m/03fwl           8\n",
       "Bird flight, flapping wings     56   /m/05_wcq           5\n",
       "Rodents, rats, mice             55    /m/06hps           6"
      ]
     },
     "execution_count": 1061,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_df.sort_values(by=['count', 'true_count'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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